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Multiscale Community-Based Fingerprinting of Signed Functional Networks — arXiv:2608.27483

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.27483
License (as archived): CC0-1.0
Archived: 2026-10-17T11:06:40.568Z
SHA-256 of the archived text: abeccd9ad0945525ddc4b438545dc2b6412d198c9d1d1712390529ae9ed683dd
Stable URL of this page: https://metraipolis.com/documento/dc_4e5400fc6db7

How to cite

Multiscale Community-Based Fingerprinting of Signed Functional Networks — arXiv:2608.27483 — arXiv (en), https://arxiv.org/abs/2608.27483 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_4e5400fc6db7 (Metraipolis archive dc_4e5400fc6db7, sha256 abeccd9ad0945525ddc4b438545dc2b6412d198c9d1d1712390529ae9ed683dd)

BibTeX

@misc{dc4e5400fc6db7,
  title = {Multiscale Community-Based Fingerprinting of Signed Functional Networks — arXiv:2608.27483},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.27483},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_4e5400fc6db7; sha256 abeccd9ad0945525ddc4b438545dc2b6412d198c9d1d1712390529ae9ed683dd},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.27483. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Objective: Recent studies demonstrate that functional connectomes contain subject-specific signatures, or \textit{fingerprints}, that can identify individuals across repeated sessions and tasks. Existing methods mostly rely on edge-level features that are sensitive to noise, difficult to interpret, and limited in their ability to generalize across tasks and datasets. Methods: We propose a multiscale community-based functional connectome fingerprinting framework that characterizes each individual by the mesoscale structure of their functional networks. We introduce a signed multilayer community detection framework that incorporates both correlated and anti-correlated brain activity to identify subject-specific community structures across tasks and sessions. Graph-theoretic metrics are then computed from the resulting joint community structures to derive low-dimensional community-level fingerprint representations. Results: The proposed framework is evaluated on 810 healthy control subjects from the Human Connectome Project (HCP). The results show that community-based fingerprints provide a reliable and interpretable substrate for individualized brain characterization across sessions and tasks. Conclusion: Mesoscale community structure provides meaningful and discriminative subject-specific fingerprints. Significance: The proposed framework offers a promising foundation for precision neuroimaging and personalized neuroscience applications.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-31 in cs.LG).
Categorie arXiv: Neurons and Cognition (q-bio.NC) ; Machine Learning (cs.LG); Signal Processing (eess.SP).
Pagina del lavoro: https://arxiv.org/abs/2608.27483 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T10:50:25.825Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-08-31?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).